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Create Lookalike Audience

meta_create_lookalike_audience

Creates a lookalike audience from a source custom audience, targeting users similar to existing customers in a specified country with a defined size ratio.

Instructions

Creates a lookalike audience based on an existing custom audience or page.

Args:

  • ad_account_id (string): Ad account ID

  • name (string): Audience name

  • origin_audience_id (string): Source custom audience ID to base the lookalike on

  • country (string): ISO 3166-1 alpha-2 country code (e.g., "US", "GB")

  • ratio (number): Lookalike size as fraction of country population (0.01–0.20, i.e. 1%–20%)

Returns the new lookalike audience ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ad_account_idYes
nameYes
origin_audience_idYesSource custom audience ID
countryYesISO country code (e.g., US)
ratioYesAudience size ratio (0.01–0.20)
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide no safety hints and description adds minimal behavioral context beyond stating it creates something. No mention of side effects, idempotency, or authentication needs. The description does not contradict annotations but adds little value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose, concise with a brief paragraph and a clean arg list. However, the arg list largely repeats schema information, which could be seen as redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While it notes the return value (the new lookalike audience ID), it lacks prerequisites, error conditions, or performance considerations. Given no output schema, the description could provide more context on expected outcomes and constraints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful explanations for parameters with missing or brief schema descriptions, especially 'ad_account_id' and 'name' lacking schema descriptions. It enriches 'origin_audience_id', 'country', and 'ratio' with more specific details. Only 'response_format' is not mentioned, but it has a full schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('creates') and resource ('lookalike audience') with a specific basis ('based on an existing custom audience or page'). It distinguishes from sibling tools like meta_create_custom_audience and meta_create_saved_audience.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It does not explain use cases, prerequisites, or trade-offs compared to other audience creation tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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